Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

ABSTRACT: Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables a timely diagnosis, facilitates precision therapy, and guides prognosis. Functional characterization presents a bottleneck in translational medicine. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. Here, we describe a multi-task multi-kernel learning framework capable of harmonizing functional results and structural information with clinical phenotypes. This novel approach extends the human phenotype ontology towards kernel-based supervised machine learning. Our gain- or los

SUBMITTER: Boßelmann CM 

PROVIDER: S-EPMC10019634 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature